Method and system for training image-alignment procedures in computing environment

ABSTRACT

The present subject matter refers a method for training image-alignment procedures in a computing environment. The method comprises communicating one or more images of an object to a user and receiving a plurality of user-selected zones within said one or more through a user-interface. An augmented data-set is generated based on said one or more images comprising the user-selected zones, wherein such augmented data set comprises a plurality of additional images defining variants of said one or more communicated images. Thereafter, a machine-learning based image alignment is trained based on at-least one of the augmented data set and the communicated images.

TECHNICAL FIELD

The present invention generally relates to digital image processing,specifically towards processing an image with respect to itsimage-frame.

BACKGROUND

In state of the art systems and methods of pre-processing adistorted-image, the distorted image is aligned with a reference imagebefore computing a quality metric during inspection. The process ofaligning a distorted image with a reference image may also variously bereferred to as “registering” or “registration”. The methods of imagequality assessment of a distorted image are based on a specific form ofcomparison of the distorted image with a reference image. The alignmentfacilitates existing full reference quality-assessment methods togenerate a quality metric that corresponds with a visual-assessment.

More specifically, in any visual inspection task, one of theinitial-steps is to localize the object or determine the location of theobject within the image. This at-least facilitates an accuratequality-analysis of the object, either by AI or any other rule-basedautomated or semi-automated systems. As the manufactured-objects may beof any arbitrary-shape, the image-alignment procedures are not genericand rather specific to shape objects. For example, the polygonal-shapedobjects are aligned with a procedure or method that issubstantially-different from elliptical objects.

To put it differently, alignment depends on the shape of the object.Different objects require different alignment methods: e.g. rectangularobject, circular object etc. Alignment of objects on a predeterminedposition of the image is very helpful as it helps image inspection basedartificial intelligence (AI) model learn and generalize better.

Accordingly, it is commonplace to spend-time and energy and developcustomized-alignment methods that are specifically fine-tuned for eachobject shape/type. Having said so, one usually ends up spending too-muchtime in creating separate new alignment-algorithms for each type ofobject.

There lies at-least a need for an adjustable method for assistingalignment of objects in an image-frame in visual-inspection tasks.

There lies a need for a generic and effective method for assistingalignment of any type of non-deformable objects in visual-inspectiontasks.

SUMMARY

This summary is provided to introduce a selection of concepts in asimplified format that are further described in the detailed descriptionof the present disclosure. This summary is not intended to identify keyor essential inventive concepts of the claimed subject matter, nor is itintended for determining the scope of the claimed subject matter. Inaccordance with the purposes of the disclosure, the present disclosureas embodied and broadly described herein, describes method and systemfor predicting a condition of living-being in an environment.

The present subject matter refers a method for training image-alignmentprocedures in a computing environment. The method comprisescommunicating one or more images of an object to a user and receiving aplurality of user-selected zones within said one or more through auser-interface. An augmented data-set is generated based on said one ormore images comprising the user-selected zones, wherein such augmenteddata set comprises a plurality of additional images defining variants ofsaid one or more communicated images. Thereafter, a machine-learningbased image alignment is trained based on at-least one of the augmenteddata set and the communicated images.

In accordance with other embodiment, the present subject matter refers amethod for training image-alignment procedures for facilitatingimage-inspection. The method comprises communicating one or more imagesof an object to a user and receiving a plurality of user-selected zoneswithin said one or more images through a user-interface. An augmenteddata is generated based on said one or more image comprising theuser-selected zones, wherein the augmented data-set comprises aplurality of additional images defining variants of said one or morecommunicated images. Thereafter, a machine-learning based imagealignment method is trained based on at-least one of the augmented dataset and the communicated images. The machine-learning based imagealignment method is executed with respect to a real-time image data andthereafter the aligned images are communicated for image-inspection.

The present subject matter at-least proposes a generic-method forenabling image alignment for in turn facilitating visual-inspectiontasks, irrespective of any type and shape of objects. In an example andwithout limiting the scope of the present subject matter, the presentsubject matter at-least provides a generic deep-learning based,“minimally-supervised” method for 2D (affine) alignment of any type ofnon-deformable objects in visual inspection tasks.

The objects and advantages of the embodiments will be realized andachieved at-least by the elements, features, and combinationsparticularly pointed out in the claims. It is to be understood that boththe foregoing general description and the following detailed descriptionare representative and explanatory and are not restrictive of theinvention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

Further, skilled artisans will appreciate that elements in the drawingsare illustrated for simplicity and may not have been necessarily beendrawn to scale. For example, the flow charts illustrate the method interms of the most prominent steps involved to help to improveunderstanding of aspects of the present invention. Furthermore, in termsof the construction of the device, one or more components of the devicemay have been represented in the drawings by conventional symbols, andthe drawings may show only those specific details that are pertinent tounderstanding the embodiments of the present invention so as not toobscure the drawings with details that will be readily apparent to thoseof ordinary skill in the art having benefit of the description herein.

FIG. 1 illustrates a method for training image0alignment procedure, inaccordance with the embodiment of the present disclosure.

FIG. 2 illustrates a system based on the method of FIG. 1, in accordancewith an embodiment of the present disclosure.

FIGS. 3A and 3B illustrate an example implementation of method steps, inaccordance with another embodiment of the present disclosure.

FIGS. 4A and 4B illustrate an example implementation of method steps, inaccordance with another embodiment of the present disclosure.

FIGS. 5A and 5B illustrate another example implementation of methodsteps, in accordance with another embodiment of the present disclosure.

FIGS. 6A and 6B illustrate an example alignment of objects in an imageframe, in accordance with another embodiment of the present disclosure.

FIGS. 7A-7E illustrate example objects with user-selected zones, inaccordance with another embodiment of the present disclosure.

FIG. 8 illustrates an implementation of the system as illustrated inpreceding figures in a computing environment, in accordance with anotherembodiment of the present disclosure.

The elements in the drawings are illustrated for simplicity and may nothave been necessarily been drawn to scale. Furthermore, in terms of theconstruction of the device, one or more components of the device mayhave been represented in the drawings by conventional symbols, and thedrawings may show only those specific details that are pertinent tounderstanding the embodiments of the present disclosure so as not toobscure the drawings with details that will be readily apparent to thoseof ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of theinvention, reference will now be made to the embodiment illustrated inthe drawings and specific language will be used to describe the same. Itwill nevertheless be understood that no limitation of the scope of theinvention is thereby intended, such alterations and furthermodifications in the illustrated system, and such further applicationsof the principles of the invention as illustrated therein beingcontemplated as would normally occur to one skilled in the art to whichthe invention relates.

It will be understood by those skilled in the art that the foregoinggeneral description and the following detailed description areexplanatory of the invention and are not intended to be restrictivethereof.

Reference throughout this specification to “an aspect”, “another aspect”or similar language means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, appearancesof the phrase “in an embodiment”, “in another embodiment” and similarlanguage throughout this specification may, but do not necessarily, allrefer to the same embodiment.

The terms “comprises”, “comprising”, or any other variations thereof,are intended to cover a non-exclusive inclusion, such that a process ormethod that comprises a list of steps does not include only those stepsbut may include other steps not expressly listed or inherent to suchprocess or method. Similarly, one or more devices or sub-systems orelements or structures or components proceeded by “comprises . . . a”does not, without more constraints, preclude the existence of otherdevices or other sub-systems or other elements or other structures orother components or additional devices or additional sub-systems oradditional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skilledin the art to which this invention belongs. The system, methods, andexamples provided herein are illustrative only and not intended to belimiting.

Embodiments of the present subject matter are described below in detailwith reference to the accompanying drawings.

FIG. 1 illustrates a method for training image-alignment procedures in acomputing environment. The method comprises communicating (step 102) oneor more images of an object to a user. In an implementation, suchcommunication of the one or more images to the user comprisesshortlisting the image having a substantially less-distortion to therebyfacilitate an ease of selection of the zones by the user through theuser interface. The communicated images as shortlisted may constitutetraining data.

The method further comprises receiving (step 104) a plurality ofuser-selected zones within said one or more through a user-interface.The plurality of user selected-zones are defined by one or more of aplurality of point-selections within the communicated images or theuser-selected zones may be the plurality of portions selected by a userwithin the communicated images. The plurality of user selected-zoneswithin the communicated images are defined by at least one of: aplurality of corners of the object, a plurality of edges of the object,one or more boundaries of the object, and a free-form shape drawn by theuser within the communicated-images to localize the object. Theplurality of user selected-zones within the communicated-imagescorresponds to a set of common-features of the object across theplurality of images.

The method further comprises (step 106) generating an augmented-data setbased on the one or more images comprising the user-selected zones. Theaugmented data-set comprises a plurality of additional-images definingvariants of said one or more communicated images. The variants of theimages are obtained by identifying the object within the one or morecommunicated images based on the user-selected zone of step (104). Theidentified object within one or more communicated images is subjected tothe at-least one of: rotation, inversion, scaling, and displacement.Thereafter, the variants of the one or more communicated-images forminga part of the augmented data set are generated based on said type ofsubjection meted out to the identified object.

The method further comprises training (step 108) a machine-learningbased image-alignment method (e.g. deep learning) based on at-least oneof the augmented data-set and the communicated images. Such communicatedone or more images and the augmented data set define at least one of: atraining data set, a validation data set; and a testing data set.

In an implementation, the machine learning (ML) based image alignmentmethod comprises operating upon a generic ML image alignment procedurethrough undergoing training through a training data set as a part oftraining phase, detecting an object within an real-time image data set;and modifying a position of the detected object with respect to an imageframe within the real-time image data set to thereby align the objectwithin the image-frame in accordance with a pre-defined standard. Themachine learning based image alignment method is at least one of: a deeplearning procedure and a convolution neural network.

The method further comprises executing the machine-learning based imagealignment method with respect to real-time image data, and communicatingthe aligned images with respect to the real-time image data to animage-inspection procedure as a part of image quality-control process.

FIG. 2 illustrates a schematic-architecture or a system 200 for trainingimage-alignment procedures in a computing environment. The system 200comprises a communication-interface and GUI 202 (e.g. annotation tool)for executing the method steps 102 and 104, and a processor 204 forexecuting the steps 106 and 108. Likewise, there may be a miscellaneousmodule 206 within the system 200 that facilitateoperational-interconnection among 202 till 204, and performs otherancillary-functions.

FIGS. 3A and 3B illustrates example process steps for trainingimage-alignment procedures for facilitating image-inspection.

FIG. 3A illustrates an example step of communicating (step 302) one ormore shortlisted images of an object to a user and accordinglycorresponds to step 102. The communication of the one or more images tothe user comprises shortlisting the image having a substantiallyless-distortion (or noise) to thereby facilitate an ease of selection ofthe zones by the user through the user-interface or annotation tool.Such shortlisting may be defined by a user performed manual shortlistingof the images (say 2 images) out of the plurality of images. In otherexample, that refers automated shortlisting, a dendrogram may beexecuted to cause automatic shortlisting of distortion free or noiselessimages.

FIG. 3B illustrates receiving (step 304) a plurality of user-selectedzones within said one or more images through a user-interface andaccordingly corresponds to step 104. The plurality of userselected-zones are defined by two or more point-selections executed bythe user within the communicated images. Such plurality of userselected-zones within the communicated images corresponds to a set ofcommon-features of the object across the plurality of images. In anexample, the point selections in one image map with the selections inother image both in terms of type and position. A point that appearscommonly across images may a corner, centroid, a center, eccentricity,an offset point from centre etc.

FIGS. 4A and 4B illustrate an example implementation of method steps, inaccordance with another embodiment of the present disclosure. FIGS. 4Aand 4B illustrates example process steps for training image-alignmentprocedures for facilitating image-inspection and corresponds to step 106of FIG. 1.

FIG. 4A depicts the image with user-selected zones of FIG. 3B. In anexample, an annotation tool that renders a user-interface for drawinguser-selected zones save the position of these point selection as“ground truth” in the realm of machine learning. Subsequently, FIG. 4Bcorresponds to generating an augmented data set based on said one ormore image comprising the user-selected zones. The augmented data setcomprises a plurality of additional images defining variants of said oneor more communicated images. The same includes subjecting the objectwithin one or more communicated images to automatically undergo one ormore image editing techniques defined by at-least one of: rotation,inversion, scaling, displacement. The variants of the one or morecommunicated images forming a part of the augmented data set aregenerated based on said image-editing techniques.

In an example, the annotation tool starts data augmentation by rotating,shifting and changing the aspect ratio of the object detected in theimage as bounded by user-selected zones. For each image, 3970 images fortraining may be obtained in an example.

FIGS. 5A and 5B illustrate another example-implementation of methodsteps, in accordance with another embodiment of the present disclosure.More specifically. FIGS. 5A and 5B illustrates the method step 106 ofFIG. 1 and the process as depicted in FIGS. 4A and 4B.

In an example, thousands of new synthetic images at different positionsand rotations may be created from the annotated two or more imagesequivalent to what is represented in FIG. 5A. A single annotated imageof FIG. 5A through rotation may lead to many annotated images as a partof data augmentation for alignment as depicted in FIG. 5B. Accordingly,based on augmented data set or synthetic data acting as the training,validation, and test data, a generic AI alignment system (e.g. a deeplearning system) may be trained.

FIGS. 6A and 6B illustrate an example alignment of objects in an imageframe, in accordance with another embodiment of the present disclosure.FIG. 6A illustrates unseen images or real-time data having objects inthe images, such that objects need to be detected and aligned within theimage frame before undergoing image inspection.

For such purposes, a machine-learning based image alignment method istrained based on at least one of the augmented data set and thecommunicated images in accordance with the method step 108. Such trainedmachine-learning based image alignment method is thereafter executedwith respect to the real-time image data or unseen images in FIG. 6A forthereby obtaining aligned images for image inspection as depicted inFIG. 6B.

Finally, the one or more aligned images obtained in FIG. 6B arecommunicated to an image-inspection method for enabling aquality-control of the real-time image data. The image-inspection methodis a machine learning method for certifying an image of an object asacceptable, permissible, unacceptable, and prone to be rejected, proneto be accepted.

FIGS. 7A-7E illustrate example objects with user-selected zones, inaccordance with another embodiment of the present disclosure andaccordingly depicts step 104 of FIG. 1. In an example, the annotationtool may be used by the user to select zones differently than pointselections.

FIG. 7A depicts annotation of a triangular object by a user by selectionof a plurality of corners of the triangular object by drawing circularportions within the communicated images. Likewise, the user may alsoselect edges or boundary of the triangular object as shown in FIG. 7Band FIG. 7C by a free form or geometrical shape drawn by the user withinthe communicated images to localize the object.

With respect to the object exhibiting a circular or elliptical shape asshown in FIG. 7D, the user may annotate the boundaries by a free-formboundary drawn by a user around the object as shown in FIG. 7D. Withrespect to the object exhibiting a circular or elliptical shape as shownin FIG. 7E, the user may also resort to positioning different shapes ofpoint selections as landmarks or annotations to localize the circularobject. This will ensure the augmentation phase of step 106 replicatingthe landmarks at the user-selected positions only upon the selectedobject, despite the rotation of images.

FIG. 8 illustrates an implementation of the system 200 as illustrated inFIG. 2 in a computing environment. The present figure essentiallyillustrates the hardware configuration of the system 200 in the form ofa computer system 800 is shown. The computer system 800 can include aset of instructions that can be executed to cause the computer system800 to perform any one or more of the methods disclosed. The computersystem 800 may operate as a standalone device or may be connected, e.g.,using a network, to other computer systems or peripheral devices.

In a networked deployment, the computer system 800 may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 800 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a personal digital assistant (PDA),a mobile device, a palmtop computer, a laptop computer, a desktopcomputer, a communications device, a wireless telephone, a land-linetelephone, a web appliance, a network router, switch or bridge, or anyother machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single computer system 800 is illustrated, the term “system”shall also be taken to include any collection of systems or sub-systemsthat individually or jointly execute a set, or multiple sets, ofinstructions to perform one or more computer functions.

The computer system 800 may include a processor 802 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), or both. Theprocessor 802 may be a component in a variety of systems. For example,the processor 802 may be part of a standard personal computer or aworkstation. The processor 802 may be one or more general processors,digital signal processors, application specific integrated circuits,field programmable gate arrays, servers, networks, digital circuits,analog circuits, combinations thereof, or other now known or laterdeveloped devices for analyzing and processing data The processor 802may implement a software program, such as code generated manually (i.e.,programmed).

The computer system 800 may include a memory 804, such as a memory 804that can communicate via a bus 808. The memory 804 may be a main memory,a static memory, or a dynamic memory. The memory 804 may include, but isnot limited to computer readable storage media such as various types ofvolatile and non-volatile storage media, including but not limited torandom access memory, read-only memory, programmable read-only memory,electrically programmable read-only memory, electrically erasableread-only memory, flash memory, magnetic tape or disk, optical media andthe like. In one example, the memory 804 includes a cache or randomaccess memory for the processor 802. In alternative examples, the memory804 is separate from the processor 802, such as a cache memory of aprocessor, the system memory, or other memory. The memory 804 may be anexternal storage device or database for storing data. Examples include ahard drive, compact disc (“CD”), digital video disc (“DVD”), memorycard, memory stick, floppy disc, universal serial bus (“USB”) memorydevice, or any other device operative to store data. The memory 804 isoperable to store instructions executable by the processor 802. Thefunctions, acts or tasks illustrated in the figures or described may beperformed by the programmed processor 802 executing the instructionsstored in the memory 804. The functions, acts or tasks are independentof the particular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firm-ware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 800 may or may not further include adisplay unit 810, such as a liquid crystal display (LCD), an organiclight emitting diode (OLED), a flat panel display, a solid statedisplay, a cathode ray tube (CRT), a projector, a printer or other nowknown or later developed display device for outputting determinedinformation. The display 810 may act as an interface for the user to seethe functioning of the processor 802, or specifically as an interfacewith the software stored in the memory 804 or in the drive unit 816.

Additionally, the computer system 800 may include an input device 812configured to allow a user to interact with any of the components ofsystem 800. The input device 812 may be a number pad, a keyboard, or acursor control device, such as a mouse, or a joystick, touch screendisplay, remote control or any other device operative to interact withthe computer system 800.

The computer system 800 may also include a disk or optical drive unit816. The disk drive unit 816 may include a computer-readable medium 822in which one or more sets of instructions 824, e.g. software, can beembedded. Further, the instructions 824 may embody one or more of themethods or logic as described. In a particular example, the instructions824 may reside completely, or at least partially, within the memory 804or within the processor 802 during execution by the computer system 800.The memory 804 and the processor 802 also may include computer-readablemedia as discussed above.

The present invention contemplates a computer-readable medium thatincludes instructions 824 or receives and executes instructions 824responsive to a propagated signal so that a device connected to anetwork 826 can communicate voice, video, audio, images or any otherdata over the network 826. Further, the instructions 824 may betransmitted or received over the network 826 via a communication port orinterface 820 or using a bus 808. The communication port or interface820 may be a part of the processor 802 or may be a separate component.The communication port 820 may be created in software or may be aphysical connection in hardware. The communication port 820 may beconfigured to connect with a network 826, external media, the display810, or any other components in system 800 or combinations thereof. Theconnection with the network 826 may be a physical connection, such as awired Ethernet connection or may be established wirelessly as discussedlater. Likewise, the additional connections with other components of thesystem 800 may be physical connections or may be established wirelessly.The network 826 may alternatively be directly connected to the bus 808.

The network 826 may include wired networks, wireless networks, EthernetAVB networks, or combinations thereof. The wireless network may be acellular telephone network, an 802.11, 802.16, 802.20, 802.1Q or WiMaxnetwork. Further, the network 826 may be a public network, such as theInternet, a private network, such as an intranet, or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to TCP/IP based networkingprotocols.

In an alternative example, dedicated hardware implementations, such asapplication specific integrated circuits, programmable logic arrays andother hardware devices, can be constructed to implement various parts ofthe system 800.

The present subject matter renders a generic method for 2D imagealignment for visual inspection tasks. The method can be used for anytype and shape of objects. In an example, the method is based onminimally-supervised learning. An automatic method in accordance withthe present subject matter proposes very few (say 1-10) representativeimages for landmark annotation to the user. A deep learning based methodmay be used to align other similar images based on the landmarksannotated by the human operator. Overall, the present subject matterexpedites an overall development time for AI visual inspection by usingan adjustable generic AI alignment

Terms used in this disclosure and especially in the appended claims(e.g., bodies of the appended claims) are generally intended as “open”terms (e.g., the term “including” should be interpreted as “including,but not limited to,” the term “having” should be interpreted as “havingat least,” the term “includes” should be interpreted as “includes, butis not limited to,” etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation, no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” isused, in general such a construction is intended to include A alone, Balone, C alone, A and B together, A and C together, B and C together, orA, B, and C together, etc. For example, the use of the term “and/or” isintended to be construed in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description of embodiments, claims, ordrawings, should be understood to contemplate the possibilities ofincluding one of the terms, either of the terms or both terms. Forexample, the phrase “A or B” should be understood to include thepossibilities of “A” or “B” or “A and B.”

All examples and conditional language recited in this disclosure areintended for pedagogical objects to aid the reader in understanding theinvention and the concepts contributed by the inventor to furthering theart and are to be construed as being without limitation to suchspecifically recited examples and conditions. Although embodiments ofthe present disclosure have been described in detail, it should beunderstood that various changes, substitutions, and alterations could bemade thereto without departing from the spirit and scope of the presentdisclosure.

What is claimed is:
 1. A method for training image-alignment proceduresin a computing environment, said method comprising: communicating one ormore images of an object to a user; receiving a plurality ofuser-selected zones within said one or more through a user-interface;generating an augmented data set based on said one or more imagescomprising the user-selected zones, said augmented data set comprising aplurality of additional images defining variants of said one or morecommunicated images; and training a machine-learning based imagealignment method based on at-least one of the augmented data set and thecommunicated images.
 2. The method as claimed in claim 1, wherein theplurality of user selected-zones are defined by at least one of: i. aplurality of point-selections within the communicated images; and ii. aplurality of portions selected by a user within the communicated images.3. The method as claimed in claim 1, further comprising: identifying theobject within the one or more communicated images based on theuser-selected zone; subjecting the identified object within one or morecommunicated images to at least one of: rotation, inversion, scaling,displacement; and generating the variants of the one or morecommunicated images forming a part of the augmented data set based onsaid subjection to the identified object.
 4. The method as claimed inclaim 1, wherein the communication of the one or more images to the usercomprises shortlisting the image having a substantially less distortionto thereby facilitate an ease of selection of the zones by the userthrough the user interface.
 5. The method as claimed in claim 1, whereinsaid communicated one or more images and the augmented data set defineat least one of: a training data set; a validation data set; and atesting data set.
 6. The method as claimed in claim 1, furthercomprising: executing the machine-learning based image alignment methodwith respect to real-time image data; and communicating the alignedimages with respect to the real-time image data to an image-inspectionprocedure as a part of image quality-control process.
 7. The method asclaimed in claim 1, wherein the plurality of user selected-zones withinthe communicated images are defined by at least one of: a plurality ofcorners of the object; a plurality of edges of the object; one or moreboundaries of the object; and a free form shape drawn by the user withinthe communicated images to localize the object.
 8. The method as claimedin claim 7, wherein the plurality of user selected-zones within thecommunicated images correspond to a set of common-features of the objectacross the plurality of images.
 9. The method as claimed in claim 1,wherein the machine learning (ML) based image alignment method comprisesoperating upon a generic ML image alignment procedure through one ormore of: undergoing training through a training data set as a part oftraining phase; detecting an object within an real-time image data set;and modifying a position of the detected object with respect a framewithin the real-time image data set to thereby align the object withinthe frame in accordance with a pre-defined standard.
 10. The method asclaimed in claim 9, wherein said machine learning based image alignmentmethod is at least one of: a deep learning procedure and a convolutionneural network.
 11. A method for training image-alignment procedures forfacilitating image-inspection, comprising: communicating one or moreimages of an object to a user; receiving a plurality of user-selectedzones within said one or more images through a user-interface;generating an augmented data set based on said one or more imagecomprising the user-selected zones, said augmented data set comprising aplurality of additional images defining variants of said one or morecommunicated images; training a machine-learning based image alignmentmethod based on at least one of the augmented data set and thecommunicated images; and executing the machine-learning based imagealignment method with respect to a real-time image data andcommunicating aligned images for image inspection.
 12. The method asclaimed in claim 1, wherein the plurality of user selected-zones aredefined by two or more point-selections executed by the user within thecommunicated images.
 13. The method as claimed in claim 1, furthercomprising: subjecting the object within one or more communicated imagesto automatically undergo one or more image editing techniques defined byat least one of: rotation, inversion, scaling, displacement; andgenerating the variants of the one or more communicated images forming apart of the augmented data set based on said image-editing techniques.14. The method as claimed in claim 1, wherein the communication of theone or more images to the user comprises shortlisting the image having asubstantially less distortion to thereby facilitate an ease of selectionof the zones by the user through the user-interface, said shortlistingdefined by one or more of: a user performed shortlisting through manualaction; and an execution of dendrogram to cause automatic shortlisting.15. The method as claimed in claim 1, wherein the plurality of userselected-zones within the communicated images are defined by: aplurality of corners or edges in case of a polygonal object; a free-formboundary drawn by a user around the object exhibiting a circular orelliptical shape.
 16. The method as claimed in claim 15, wherein theplurality of user selected-zones within the communicated imagescorrespond to a set of common-features of the object across theplurality of images.
 17. The method as claimed in claim 1, furthercomprising: obtaining one or more aligned images from an operation ofthe machine-learning based image alignment procedure upon a real-timeimage data set of an object; communicating the aligned-images to animage-inspection method for enabling a quality-control of the real-timeimage data.
 18. The method as claimed in claim 17, wherein theimage-inspection method is a machine learning method for certifying animage of an object as acceptable, permissible, unacceptable, prone to berejected, prone to be accepted.
 19. A non-transitory medium comprisingcomputer-executable instructions which, when performed by processorcause the processor to train image-alignment procedures for facilitatingimage-inspection by the steps of: communicating one or more images of anobject to a user; receiving a plurality of user-selected zones withinsaid one or more images through a user-interface; generating anaugmented data set based on said one or more image comprising theuser-selected zones, said augmented data set comprising a plurality ofadditional images defining variants of said one or more communicatedimages; training a machine-learning based image alignment method basedon at least one of the augmented data set and the communicated images;and executing the machine-learning based image alignment method withrespect to real-time image data and communicating aligned images to animage-inspection procedure.